Machine Learning - Breast Cancer Detection
Data Set Information
Wisconsin Breast cancer data set used in this presentation is collected from university of Wisconsin hospitals, Madison Dr.William H.Wolberg. He had introduced 699 instances with 10 attributes. The class distribution is framed as Benign and malignant. There are 1 dependent variable and 9 independent variables. The values for the independent variables ranges from 1 - 10 and for class variable 2 for Benign and 4 for malignant tumor. The minimum possibilities for a person to get breast cancer is 1 and the maximum possibilities are represented by the value 10.
Features are computed from a digitized image of a fine needle aspirate (FNA) of a breast mass. They describe characteristics of the cell nuclei present in the image. More detailed description can be found here.
Attributes Information
| Attribute Name | Description | Category | Range Values |
|---|---|---|---|
| SCNo | Sample Code Number | Id | - |
| CT | Clump Thickness | Ordinal | 1-10 |
| UCSIZE | Uniformity of Cell Size | Ordinal | 1-10 |
| UCSHAPE | Uniformity of Cell Shape | Ordinal | 1-10 |
| MAF | Marginal Adhesion Fibrous: Fibrous bands tissue that form between two surfaces | Ordinal | 1-10 |
| ECSIZE | Epithelial Cell Size : Size of a single cell that forms tissues that lines the outside of the body and the passageways that lead to or from the surface | Ordinal | 1-10 |
| BN | Bare Nuclei | Ordinal | 1-10 |
| BC | Bland Chromatin: Evaluates for the presence of Bare bodies | Ordinal | 1-10 |
| NN | Normal Nucleoli | Ordinal | 1-10 |
| M | Mitoses: Cell growth | Ordinal | 1-10 |
| Dia | Diagnosis of tumours | Class | 2, 4 |
Data View
url = "https://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/breast-cancer-wisconsin.data"
#Change the column headings
data = read.csv(file = url, header = FALSE,
col.names = c("SCNo","CT", "UCSize", "UCShape", "MAF", "ECSize", "BN", "BC", "NN","M", "Dia") )
data = data %>% select(-SCNo) %>%
mutate(
Response = ifelse(Dia == 4, 1, 0),
Response = as.integer(Response),
BN = strtoi(BN)
)
kbl(data) %>%
kable_paper() %>%
scroll_box(width = "800px", height = "300px")| CT | UCSize | UCShape | MAF | ECSize | BN | BC | NN | M | Dia | Response |
|---|---|---|---|---|---|---|---|---|---|---|
| 5 | 1 | 1 | 1 | 2 | 1 | 3 | 1 | 1 | 2 | 0 |
| 5 | 4 | 4 | 5 | 7 | 10 | 3 | 2 | 1 | 2 | 0 |
| 3 | 1 | 1 | 1 | 2 | 2 | 3 | 1 | 1 | 2 | 0 |
| 6 | 8 | 8 | 1 | 3 | 4 | 3 | 7 | 1 | 2 | 0 |
| 4 | 1 | 1 | 3 | 2 | 1 | 3 | 1 | 1 | 2 | 0 |
| 8 | 10 | 10 | 8 | 7 | 10 | 9 | 7 | 1 | 4 | 1 |
| 1 | 1 | 1 | 1 | 2 | 10 | 3 | 1 | 1 | 2 | 0 |
| 2 | 1 | 2 | 1 | 2 | 1 | 3 | 1 | 1 | 2 | 0 |
| 2 | 1 | 1 | 1 | 2 | 1 | 1 | 1 | 5 | 2 | 0 |
| 4 | 2 | 1 | 1 | 2 | 1 | 2 | 1 | 1 | 2 | 0 |
| 1 | 1 | 1 | 1 | 1 | 1 | 3 | 1 | 1 | 2 | 0 |
| 2 | 1 | 1 | 1 | 2 | 1 | 2 | 1 | 1 | 2 | 0 |
| 5 | 3 | 3 | 3 | 2 | 3 | 4 | 4 | 1 | 4 | 1 |
| 1 | 1 | 1 | 1 | 2 | 3 | 3 | 1 | 1 | 2 | 0 |
| 8 | 7 | 5 | 10 | 7 | 9 | 5 | 5 | 4 | 4 | 1 |
| 7 | 4 | 6 | 4 | 6 | 1 | 4 | 3 | 1 | 4 | 1 |
| 4 | 1 | 1 | 1 | 2 | 1 | 2 | 1 | 1 | 2 | 0 |
| 4 | 1 | 1 | 1 | 2 | 1 | 3 | 1 | 1 | 2 | 0 |
| 10 | 7 | 7 | 6 | 4 | 10 | 4 | 1 | 2 | 4 | 1 |
| 6 | 1 | 1 | 1 | 2 | 1 | 3 | 1 | 1 | 2 | 0 |
| 7 | 3 | 2 | 10 | 5 | 10 | 5 | 4 | 4 | 4 | 1 |
| 10 | 5 | 5 | 3 | 6 | 7 | 7 | 10 | 1 | 4 | 1 |
| 3 | 1 | 1 | 1 | 2 | 1 | 2 | 1 | 1 | 2 | 0 |
| 8 | 4 | 5 | 1 | 2 | NA | 7 | 3 | 1 | 4 | 1 |
| 1 | 1 | 1 | 1 | 2 | 1 | 3 | 1 | 1 | 2 | 0 |
| 5 | 2 | 3 | 4 | 2 | 7 | 3 | 6 | 1 | 4 | 1 |
| 3 | 2 | 1 | 1 | 1 | 1 | 2 | 1 | 1 | 2 | 0 |
| 5 | 1 | 1 | 1 | 2 | 1 | 2 | 1 | 1 | 2 | 0 |
| 2 | 1 | 1 | 1 | 2 | 1 | 2 | 1 | 1 | 2 | 0 |
| 1 | 1 | 3 | 1 | 2 | 1 | 1 | 1 | 1 | 2 | 0 |
| 3 | 1 | 1 | 1 | 1 | 1 | 2 | 1 | 1 | 2 | 0 |
| 2 | 1 | 1 | 1 | 2 | 1 | 3 | 1 | 1 | 2 | 0 |
| 10 | 7 | 7 | 3 | 8 | 5 | 7 | 4 | 3 | 4 | 1 |
| 2 | 1 | 1 | 2 | 2 | 1 | 3 | 1 | 1 | 2 | 0 |
| 3 | 1 | 2 | 1 | 2 | 1 | 2 | 1 | 1 | 2 | 0 |
| 2 | 1 | 1 | 1 | 2 | 1 | 2 | 1 | 1 | 2 | 0 |
| 10 | 10 | 10 | 8 | 6 | 1 | 8 | 9 | 1 | 4 | 1 |
| 6 | 2 | 1 | 1 | 1 | 1 | 7 | 1 | 1 | 2 | 0 |
| 5 | 4 | 4 | 9 | 2 | 10 | 5 | 6 | 1 | 4 | 1 |
| 2 | 5 | 3 | 3 | 6 | 7 | 7 | 5 | 1 | 4 | 1 |
| 6 | 6 | 6 | 9 | 6 | NA | 7 | 8 | 1 | 2 | 0 |
| 10 | 4 | 3 | 1 | 3 | 3 | 6 | 5 | 2 | 4 | 1 |
| 6 | 10 | 10 | 2 | 8 | 10 | 7 | 3 | 3 | 4 | 1 |
| 5 | 6 | 5 | 6 | 10 | 1 | 3 | 1 | 1 | 4 | 1 |
| 10 | 10 | 10 | 4 | 8 | 1 | 8 | 10 | 1 | 4 | 1 |
| 1 | 1 | 1 | 1 | 2 | 1 | 2 | 1 | 2 | 2 | 0 |
| 3 | 7 | 7 | 4 | 4 | 9 | 4 | 8 | 1 | 4 | 1 |
| 1 | 1 | 1 | 1 | 2 | 1 | 2 | 1 | 1 | 2 | 0 |
| 4 | 1 | 1 | 3 | 2 | 1 | 3 | 1 | 1 | 2 | 0 |
| 7 | 8 | 7 | 2 | 4 | 8 | 3 | 8 | 2 | 4 | 1 |
| 9 | 5 | 8 | 1 | 2 | 3 | 2 | 1 | 5 | 4 | 1 |
| 5 | 3 | 3 | 4 | 2 | 4 | 3 | 4 | 1 | 4 | 1 |
| 10 | 3 | 6 | 2 | 3 | 5 | 4 | 10 | 2 | 4 | 1 |
| 5 | 5 | 5 | 8 | 10 | 8 | 7 | 3 | 7 | 4 | 1 |
| 10 | 5 | 5 | 6 | 8 | 8 | 7 | 1 | 1 | 4 | 1 |
| 10 | 6 | 6 | 3 | 4 | 5 | 3 | 6 | 1 | 4 | 1 |
| 8 | 10 | 10 | 1 | 3 | 6 | 3 | 9 | 1 | 4 | 1 |
| 8 | 2 | 4 | 1 | 5 | 1 | 5 | 4 | 4 | 4 | 1 |
| 5 | 2 | 3 | 1 | 6 | 10 | 5 | 1 | 1 | 4 | 1 |
| 9 | 5 | 5 | 2 | 2 | 2 | 5 | 1 | 1 | 4 | 1 |
| 5 | 3 | 5 | 5 | 3 | 3 | 4 | 10 | 1 | 4 | 1 |
| 1 | 1 | 1 | 1 | 2 | 2 | 2 | 1 | 1 | 2 | 0 |
| 9 | 10 | 10 | 1 | 10 | 8 | 3 | 3 | 1 | 4 | 1 |
| 6 | 3 | 4 | 1 | 5 | 2 | 3 | 9 | 1 | 4 | 1 |
| 1 | 1 | 1 | 1 | 2 | 1 | 2 | 1 | 1 | 2 | 0 |
| 10 | 4 | 2 | 1 | 3 | 2 | 4 | 3 | 10 | 4 | 1 |
| 4 | 1 | 1 | 1 | 2 | 1 | 3 | 1 | 1 | 2 | 0 |
| 5 | 3 | 4 | 1 | 8 | 10 | 4 | 9 | 1 | 4 | 1 |
| 8 | 3 | 8 | 3 | 4 | 9 | 8 | 9 | 8 | 4 | 1 |
| 1 | 1 | 1 | 1 | 2 | 1 | 3 | 2 | 1 | 2 | 0 |
| 5 | 1 | 3 | 1 | 2 | 1 | 2 | 1 | 1 | 2 | 0 |
| 6 | 10 | 2 | 8 | 10 | 2 | 7 | 8 | 10 | 4 | 1 |
| 1 | 3 | 3 | 2 | 2 | 1 | 7 | 2 | 1 | 2 | 0 |
| 9 | 4 | 5 | 10 | 6 | 10 | 4 | 8 | 1 | 4 | 1 |
| 10 | 6 | 4 | 1 | 3 | 4 | 3 | 2 | 3 | 4 | 1 |
| 1 | 1 | 2 | 1 | 2 | 2 | 4 | 2 | 1 | 2 | 0 |
| 1 | 1 | 4 | 1 | 2 | 1 | 2 | 1 | 1 | 2 | 0 |
| 5 | 3 | 1 | 2 | 2 | 1 | 2 | 1 | 1 | 2 | 0 |
| 3 | 1 | 1 | 1 | 2 | 3 | 3 | 1 | 1 | 2 | 0 |
| 2 | 1 | 1 | 1 | 3 | 1 | 2 | 1 | 1 | 2 | 0 |
| 2 | 2 | 2 | 1 | 1 | 1 | 7 | 1 | 1 | 2 | 0 |
| 4 | 1 | 1 | 2 | 2 | 1 | 2 | 1 | 1 | 2 | 0 |
| 5 | 2 | 1 | 1 | 2 | 1 | 3 | 1 | 1 | 2 | 0 |
| 3 | 1 | 1 | 1 | 2 | 2 | 7 | 1 | 1 | 2 | 0 |
| 3 | 5 | 7 | 8 | 8 | 9 | 7 | 10 | 7 | 4 | 1 |
| 5 | 10 | 6 | 1 | 10 | 4 | 4 | 10 | 10 | 4 | 1 |
| 3 | 3 | 6 | 4 | 5 | 8 | 4 | 4 | 1 | 4 | 1 |
| 3 | 6 | 6 | 6 | 5 | 10 | 6 | 8 | 3 | 4 | 1 |
| 4 | 1 | 1 | 1 | 2 | 1 | 3 | 1 | 1 | 2 | 0 |
| 2 | 1 | 1 | 2 | 3 | 1 | 2 | 1 | 1 | 2 | 0 |
| 1 | 1 | 1 | 1 | 2 | 1 | 3 | 1 | 1 | 2 | 0 |
| 3 | 1 | 1 | 2 | 2 | 1 | 1 | 1 | 1 | 2 | 0 |
| 4 | 1 | 1 | 1 | 2 | 1 | 3 | 1 | 1 | 2 | 0 |
| 1 | 1 | 1 | 1 | 2 | 1 | 2 | 1 | 1 | 2 | 0 |
| 2 | 1 | 1 | 1 | 2 | 1 | 3 | 1 | 1 | 2 | 0 |
| 1 | 1 | 1 | 1 | 2 | 1 | 3 | 1 | 1 | 2 | 0 |
| 2 | 1 | 1 | 2 | 2 | 1 | 1 | 1 | 1 | 2 | 0 |
| 5 | 1 | 1 | 1 | 2 | 1 | 3 | 1 | 1 | 2 | 0 |
| 9 | 6 | 9 | 2 | 10 | 6 | 2 | 9 | 10 | 4 | 1 |
| 7 | 5 | 6 | 10 | 5 | 10 | 7 | 9 | 4 | 4 | 1 |
| 10 | 3 | 5 | 1 | 10 | 5 | 3 | 10 | 2 | 4 | 1 |
| 2 | 3 | 4 | 4 | 2 | 5 | 2 | 5 | 1 | 4 | 1 |
| 4 | 1 | 2 | 1 | 2 | 1 | 3 | 1 | 1 | 2 | 0 |
| 8 | 2 | 3 | 1 | 6 | 3 | 7 | 1 | 1 | 4 | 1 |
| 10 | 10 | 10 | 10 | 10 | 1 | 8 | 8 | 8 | 4 | 1 |
| 7 | 3 | 4 | 4 | 3 | 3 | 3 | 2 | 7 | 4 | 1 |
| 10 | 10 | 10 | 8 | 2 | 10 | 4 | 1 | 1 | 4 | 1 |
| 1 | 6 | 8 | 10 | 8 | 10 | 5 | 7 | 1 | 4 | 1 |
| 1 | 1 | 1 | 1 | 2 | 1 | 2 | 3 | 1 | 2 | 0 |
| 6 | 5 | 4 | 4 | 3 | 9 | 7 | 8 | 3 | 4 | 1 |
| 1 | 3 | 1 | 2 | 2 | 2 | 5 | 3 | 2 | 2 | 0 |
| 8 | 6 | 4 | 3 | 5 | 9 | 3 | 1 | 1 | 4 | 1 |
| 10 | 3 | 3 | 10 | 2 | 10 | 7 | 3 | 3 | 4 | 1 |
| 10 | 10 | 10 | 3 | 10 | 8 | 8 | 1 | 1 | 4 | 1 |
| 3 | 3 | 2 | 1 | 2 | 3 | 3 | 1 | 1 | 2 | 0 |
| 1 | 1 | 1 | 1 | 2 | 5 | 1 | 1 | 1 | 2 | 0 |
| 8 | 3 | 3 | 1 | 2 | 2 | 3 | 2 | 1 | 2 | 0 |
| 4 | 5 | 5 | 10 | 4 | 10 | 7 | 5 | 8 | 4 | 1 |
| 1 | 1 | 1 | 1 | 4 | 3 | 1 | 1 | 1 | 2 | 0 |
| 3 | 2 | 1 | 1 | 2 | 2 | 3 | 1 | 1 | 2 | 0 |
| 1 | 1 | 2 | 2 | 2 | 1 | 3 | 1 | 1 | 2 | 0 |
| 4 | 2 | 1 | 1 | 2 | 2 | 3 | 1 | 1 | 2 | 0 |
| 10 | 10 | 10 | 2 | 10 | 10 | 5 | 3 | 3 | 4 | 1 |
| 5 | 3 | 5 | 1 | 8 | 10 | 5 | 3 | 1 | 4 | 1 |
| 5 | 4 | 6 | 7 | 9 | 7 | 8 | 10 | 1 | 4 | 1 |
| 1 | 1 | 1 | 1 | 2 | 1 | 2 | 1 | 1 | 2 | 0 |
| 7 | 5 | 3 | 7 | 4 | 10 | 7 | 5 | 5 | 4 | 1 |
| 3 | 1 | 1 | 1 | 2 | 1 | 3 | 1 | 1 | 2 | 0 |
| 8 | 3 | 5 | 4 | 5 | 10 | 1 | 6 | 2 | 4 | 1 |
| 1 | 1 | 1 | 1 | 10 | 1 | 1 | 1 | 1 | 2 | 0 |
| 5 | 1 | 3 | 1 | 2 | 1 | 2 | 1 | 1 | 2 | 0 |
| 2 | 1 | 1 | 1 | 2 | 1 | 3 | 1 | 1 | 2 | 0 |
| 5 | 10 | 8 | 10 | 8 | 10 | 3 | 6 | 3 | 4 | 1 |
| 3 | 1 | 1 | 1 | 2 | 1 | 2 | 2 | 1 | 2 | 0 |
| 3 | 1 | 1 | 1 | 3 | 1 | 2 | 1 | 1 | 2 | 0 |
| 5 | 1 | 1 | 1 | 2 | 2 | 3 | 3 | 1 | 2 | 0 |
| 4 | 1 | 1 | 1 | 2 | 1 | 2 | 1 | 1 | 2 | 0 |
| 3 | 1 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 2 | 0 |
| 4 | 1 | 2 | 1 | 2 | 1 | 2 | 1 | 1 | 2 | 0 |
| 1 | 1 | 1 | 1 | 1 | NA | 2 | 1 | 1 | 2 | 0 |
| 3 | 1 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 2 | 0 |
| 2 | 1 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 2 | 0 |
| 9 | 5 | 5 | 4 | 4 | 5 | 4 | 3 | 3 | 4 | 1 |
| 1 | 1 | 1 | 1 | 2 | 5 | 1 | 1 | 1 | 2 | 0 |
| 2 | 1 | 1 | 1 | 2 | 1 | 2 | 1 | 1 | 2 | 0 |
| 1 | 1 | 3 | 1 | 2 | NA | 2 | 1 | 1 | 2 | 0 |
| 3 | 4 | 5 | 2 | 6 | 8 | 4 | 1 | 1 | 4 | 1 |
| 1 | 1 | 1 | 1 | 3 | 2 | 2 | 1 | 1 | 2 | 0 |
| 3 | 1 | 1 | 3 | 8 | 1 | 5 | 8 | 1 | 2 | 0 |
| 8 | 8 | 7 | 4 | 10 | 10 | 7 | 8 | 7 | 4 | 1 |
| 1 | 1 | 1 | 1 | 1 | 1 | 3 | 1 | 1 | 2 | 0 |
| 7 | 2 | 4 | 1 | 6 | 10 | 5 | 4 | 3 | 4 | 1 |
| 10 | 10 | 8 | 6 | 4 | 5 | 8 | 10 | 1 | 4 | 1 |
| 4 | 1 | 1 | 1 | 2 | 3 | 1 | 1 | 1 | 2 | 0 |
| 1 | 1 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 2 | 0 |
| 5 | 5 | 5 | 6 | 3 | 10 | 3 | 1 | 1 | 4 | 1 |
| 1 | 2 | 2 | 1 | 2 | 1 | 2 | 1 | 1 | 2 | 0 |
| 2 | 1 | 1 | 1 | 2 | 1 | 3 | 1 | 1 | 2 | 0 |
| 1 | 1 | 2 | 1 | 3 | NA | 1 | 1 | 1 | 2 | 0 |
| 9 | 9 | 10 | 3 | 6 | 10 | 7 | 10 | 6 | 4 | 1 |
| 10 | 7 | 7 | 4 | 5 | 10 | 5 | 7 | 2 | 4 | 1 |
| 4 | 1 | 1 | 1 | 2 | 1 | 3 | 2 | 1 | 2 | 0 |
| 3 | 1 | 1 | 1 | 2 | 1 | 3 | 1 | 1 | 2 | 0 |
| 1 | 1 | 1 | 2 | 1 | 3 | 1 | 1 | 7 | 2 | 0 |
| 5 | 1 | 1 | 1 | 2 | NA | 3 | 1 | 1 | 2 | 0 |
| 4 | 1 | 1 | 1 | 2 | 2 | 3 | 2 | 1 | 2 | 0 |
| 5 | 6 | 7 | 8 | 8 | 10 | 3 | 10 | 3 | 4 | 1 |
| 10 | 8 | 10 | 10 | 6 | 1 | 3 | 1 | 10 | 4 | 1 |
| 3 | 1 | 1 | 1 | 2 | 1 | 3 | 1 | 1 | 2 | 0 |
| 1 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 1 | 2 | 0 |
| 3 | 1 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 2 | 0 |
| 1 | 1 | 1 | 1 | 2 | 1 | 3 | 1 | 1 | 2 | 0 |
| 1 | 1 | 1 | 1 | 2 | 1 | 2 | 1 | 1 | 2 | 0 |
| 6 | 10 | 10 | 10 | 8 | 10 | 10 | 10 | 7 | 4 | 1 |
| 8 | 6 | 5 | 4 | 3 | 10 | 6 | 1 | 1 | 4 | 1 |
| 5 | 8 | 7 | 7 | 10 | 10 | 5 | 7 | 1 | 4 | 1 |
| 2 | 1 | 1 | 1 | 2 | 1 | 3 | 1 | 1 | 2 | 0 |
| 5 | 10 | 10 | 3 | 8 | 1 | 5 | 10 | 3 | 4 | 1 |
| 4 | 1 | 1 | 1 | 2 | 1 | 3 | 1 | 1 | 2 | 0 |
| 5 | 3 | 3 | 3 | 6 | 10 | 3 | 1 | 1 | 4 | 1 |
| 1 | 1 | 1 | 1 | 1 | 1 | 3 | 1 | 1 | 2 | 0 |
| 1 | 1 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 2 | 0 |
| 6 | 1 | 1 | 1 | 2 | 1 | 3 | 1 | 1 | 2 | 0 |
| 5 | 8 | 8 | 8 | 5 | 10 | 7 | 8 | 1 | 4 | 1 |
| 8 | 7 | 6 | 4 | 4 | 10 | 5 | 1 | 1 | 4 | 1 |
| 2 | 1 | 1 | 1 | 1 | 1 | 3 | 1 | 1 | 2 | 0 |
| 1 | 5 | 8 | 6 | 5 | 8 | 7 | 10 | 1 | 4 | 1 |
| 10 | 5 | 6 | 10 | 6 | 10 | 7 | 7 | 10 | 4 | 1 |
| 5 | 8 | 4 | 10 | 5 | 8 | 9 | 10 | 1 | 4 | 1 |
| 1 | 2 | 3 | 1 | 2 | 1 | 3 | 1 | 1 | 2 | 0 |
| 10 | 10 | 10 | 8 | 6 | 8 | 7 | 10 | 1 | 4 | 1 |
| 7 | 5 | 10 | 10 | 10 | 10 | 4 | 10 | 3 | 4 | 1 |
| 5 | 1 | 1 | 1 | 2 | 1 | 2 | 1 | 1 | 2 | 0 |
| 1 | 1 | 1 | 1 | 2 | 1 | 3 | 1 | 1 | 2 | 0 |
| 3 | 1 | 1 | 1 | 2 | 1 | 3 | 1 | 1 | 2 | 0 |
| 4 | 1 | 1 | 1 | 2 | 1 | 3 | 1 | 1 | 2 | 0 |
| 8 | 4 | 4 | 5 | 4 | 7 | 7 | 8 | 2 | 2 | 0 |
| 5 | 1 | 1 | 4 | 2 | 1 | 3 | 1 | 1 | 2 | 0 |
| 1 | 1 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 2 | 0 |
| 3 | 1 | 1 | 1 | 2 | 1 | 2 | 1 | 1 | 2 | 0 |
| 9 | 7 | 7 | 5 | 5 | 10 | 7 | 8 | 3 | 4 | 1 |
| 10 | 8 | 8 | 4 | 10 | 10 | 8 | 1 | 1 | 4 | 1 |
| 1 | 1 | 1 | 1 | 2 | 1 | 3 | 1 | 1 | 2 | 0 |
| 5 | 1 | 1 | 1 | 2 | 1 | 3 | 1 | 1 | 2 | 0 |
| 1 | 1 | 1 | 1 | 2 | 1 | 3 | 1 | 1 | 2 | 0 |
| 5 | 10 | 10 | 9 | 6 | 10 | 7 | 10 | 5 | 4 | 1 |
| 10 | 10 | 9 | 3 | 7 | 5 | 3 | 5 | 1 | 4 | 1 |
| 1 | 1 | 1 | 1 | 1 | 1 | 3 | 1 | 1 | 2 | 0 |
| 1 | 1 | 1 | 1 | 1 | 1 | 3 | 1 | 1 | 2 | 0 |
| 5 | 1 | 1 | 1 | 1 | 1 | 3 | 1 | 1 | 2 | 0 |
| 8 | 10 | 10 | 10 | 5 | 10 | 8 | 10 | 6 | 4 | 1 |
| 8 | 10 | 8 | 8 | 4 | 8 | 7 | 7 | 1 | 4 | 1 |
| 1 | 1 | 1 | 1 | 2 | 1 | 3 | 1 | 1 | 2 | 0 |
| 10 | 10 | 10 | 10 | 7 | 10 | 7 | 10 | 4 | 4 | 1 |
| 10 | 10 | 10 | 10 | 3 | 10 | 10 | 6 | 1 | 4 | 1 |
| 8 | 7 | 8 | 7 | 5 | 5 | 5 | 10 | 2 | 4 | 1 |
| 1 | 1 | 1 | 1 | 2 | 1 | 2 | 1 | 1 | 2 | 0 |
| 1 | 1 | 1 | 1 | 2 | 1 | 3 | 1 | 1 | 2 | 0 |
| 6 | 10 | 7 | 7 | 6 | 4 | 8 | 10 | 2 | 4 | 1 |
| 6 | 1 | 3 | 1 | 2 | 1 | 3 | 1 | 1 | 2 | 0 |
| 1 | 1 | 1 | 2 | 2 | 1 | 3 | 1 | 1 | 2 | 0 |
| 10 | 6 | 4 | 3 | 10 | 10 | 9 | 10 | 1 | 4 | 1 |
| 4 | 1 | 1 | 3 | 1 | 5 | 2 | 1 | 1 | 4 | 1 |
| 7 | 5 | 6 | 3 | 3 | 8 | 7 | 4 | 1 | 4 | 1 |
| 10 | 5 | 5 | 6 | 3 | 10 | 7 | 9 | 2 | 4 | 1 |
| 1 | 1 | 1 | 1 | 2 | 1 | 2 | 1 | 1 | 2 | 0 |
| 10 | 5 | 7 | 4 | 4 | 10 | 8 | 9 | 1 | 4 | 1 |
| 8 | 9 | 9 | 5 | 3 | 5 | 7 | 7 | 1 | 4 | 1 |
| 1 | 1 | 1 | 1 | 1 | 1 | 3 | 1 | 1 | 2 | 0 |
| 10 | 10 | 10 | 3 | 10 | 10 | 9 | 10 | 1 | 4 | 1 |
| 7 | 4 | 7 | 4 | 3 | 7 | 7 | 6 | 1 | 4 | 1 |
| 6 | 8 | 7 | 5 | 6 | 8 | 8 | 9 | 2 | 4 | 1 |
| 8 | 4 | 6 | 3 | 3 | 1 | 4 | 3 | 1 | 2 | 0 |
| 10 | 4 | 5 | 5 | 5 | 10 | 4 | 1 | 1 | 4 | 1 |
| 3 | 3 | 2 | 1 | 3 | 1 | 3 | 6 | 1 | 2 | 0 |
| 3 | 1 | 4 | 1 | 2 | NA | 3 | 1 | 1 | 2 | 0 |
| 10 | 8 | 8 | 2 | 8 | 10 | 4 | 8 | 10 | 4 | 1 |
| 9 | 8 | 8 | 5 | 6 | 2 | 4 | 10 | 4 | 4 | 1 |
| 8 | 10 | 10 | 8 | 6 | 9 | 3 | 10 | 10 | 4 | 1 |
| 10 | 4 | 3 | 2 | 3 | 10 | 5 | 3 | 2 | 4 | 1 |
| 5 | 1 | 3 | 3 | 2 | 2 | 2 | 3 | 1 | 2 | 0 |
| 3 | 1 | 1 | 3 | 1 | 1 | 3 | 1 | 1 | 2 | 0 |
| 2 | 1 | 1 | 1 | 2 | 1 | 3 | 1 | 1 | 2 | 0 |
| 1 | 1 | 1 | 1 | 2 | 5 | 5 | 1 | 1 | 2 | 0 |
| 1 | 1 | 1 | 1 | 2 | 1 | 3 | 1 | 1 | 2 | 0 |
| 5 | 1 | 1 | 2 | 2 | 2 | 3 | 1 | 1 | 2 | 0 |
| 8 | 10 | 10 | 8 | 5 | 10 | 7 | 8 | 1 | 4 | 1 |
| 8 | 4 | 4 | 1 | 2 | 9 | 3 | 3 | 1 | 4 | 1 |
| 4 | 1 | 1 | 1 | 2 | 1 | 3 | 6 | 1 | 2 | 0 |
| 3 | 1 | 1 | 1 | 2 | NA | 3 | 1 | 1 | 2 | 0 |
| 1 | 2 | 2 | 1 | 2 | 1 | 1 | 1 | 1 | 2 | 0 |
| 10 | 4 | 4 | 10 | 2 | 10 | 5 | 3 | 3 | 4 | 1 |
| 6 | 3 | 3 | 5 | 3 | 10 | 3 | 5 | 3 | 2 | 0 |
| 6 | 10 | 10 | 2 | 8 | 10 | 7 | 3 | 3 | 4 | 1 |
| 9 | 10 | 10 | 1 | 10 | 8 | 3 | 3 | 1 | 4 | 1 |
| 5 | 6 | 6 | 2 | 4 | 10 | 3 | 6 | 1 | 4 | 1 |
| 3 | 1 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 2 | 0 |
| 3 | 1 | 1 | 1 | 2 | 1 | 2 | 1 | 1 | 2 | 0 |
| 3 | 1 | 1 | 1 | 2 | 1 | 3 | 1 | 1 | 2 | 0 |
| 5 | 7 | 7 | 1 | 5 | 8 | 3 | 4 | 1 | 2 | 0 |
| 10 | 5 | 8 | 10 | 3 | 10 | 5 | 1 | 3 | 4 | 1 |
| 5 | 10 | 10 | 6 | 10 | 10 | 10 | 6 | 5 | 4 | 1 |
| 8 | 8 | 9 | 4 | 5 | 10 | 7 | 8 | 1 | 4 | 1 |
| 10 | 4 | 4 | 10 | 6 | 10 | 5 | 5 | 1 | 4 | 1 |
| 7 | 9 | 4 | 10 | 10 | 3 | 5 | 3 | 3 | 4 | 1 |
| 5 | 1 | 4 | 1 | 2 | 1 | 3 | 2 | 1 | 2 | 0 |
| 10 | 10 | 6 | 3 | 3 | 10 | 4 | 3 | 2 | 4 | 1 |
| 3 | 3 | 5 | 2 | 3 | 10 | 7 | 1 | 1 | 4 | 1 |
| 10 | 8 | 8 | 2 | 3 | 4 | 8 | 7 | 8 | 4 | 1 |
| 1 | 1 | 1 | 1 | 2 | 1 | 3 | 1 | 1 | 2 | 0 |
| 8 | 4 | 7 | 1 | 3 | 10 | 3 | 9 | 2 | 4 | 1 |
| 5 | 1 | 1 | 1 | 2 | 1 | 3 | 1 | 1 | 2 | 0 |
| 3 | 3 | 5 | 2 | 3 | 10 | 7 | 1 | 1 | 4 | 1 |
| 7 | 2 | 4 | 1 | 3 | 4 | 3 | 3 | 1 | 4 | 1 |
| 3 | 1 | 1 | 1 | 2 | 1 | 3 | 2 | 1 | 2 | 0 |
| 3 | 1 | 3 | 1 | 2 | NA | 2 | 1 | 1 | 2 | 0 |
| 3 | 1 | 1 | 1 | 2 | 1 | 2 | 1 | 1 | 2 | 0 |
| 1 | 1 | 1 | 1 | 2 | 1 | 2 | 1 | 1 | 2 | 0 |
| 1 | 1 | 1 | 1 | 2 | 1 | 3 | 1 | 1 | 2 | 0 |
| 10 | 5 | 7 | 3 | 3 | 7 | 3 | 3 | 8 | 4 | 1 |
| 3 | 1 | 1 | 1 | 2 | 1 | 3 | 1 | 1 | 2 | 0 |
| 2 | 1 | 1 | 2 | 2 | 1 | 3 | 1 | 1 | 2 | 0 |
| 1 | 4 | 3 | 10 | 4 | 10 | 5 | 6 | 1 | 4 | 1 |
| 10 | 4 | 6 | 1 | 2 | 10 | 5 | 3 | 1 | 4 | 1 |
| 7 | 4 | 5 | 10 | 2 | 10 | 3 | 8 | 2 | 4 | 1 |
| 8 | 10 | 10 | 10 | 8 | 10 | 10 | 7 | 3 | 4 | 1 |
| 10 | 10 | 10 | 10 | 10 | 10 | 4 | 10 | 10 | 4 | 1 |
| 3 | 1 | 1 | 1 | 3 | 1 | 2 | 1 | 1 | 2 | 0 |
| 6 | 1 | 3 | 1 | 4 | 5 | 5 | 10 | 1 | 4 | 1 |
| 5 | 6 | 6 | 8 | 6 | 10 | 4 | 10 | 4 | 4 | 1 |
| 1 | 1 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 2 | 0 |
| 1 | 1 | 1 | 1 | 2 | 1 | 3 | 1 | 1 | 2 | 0 |
| 8 | 8 | 8 | 1 | 2 | NA | 6 | 10 | 1 | 4 | 1 |
| 10 | 4 | 4 | 6 | 2 | 10 | 2 | 3 | 1 | 4 | 1 |
| 1 | 1 | 1 | 1 | 2 | NA | 2 | 1 | 1 | 2 | 0 |
| 5 | 5 | 7 | 8 | 6 | 10 | 7 | 4 | 1 | 4 | 1 |
| 5 | 3 | 4 | 3 | 4 | 5 | 4 | 7 | 1 | 2 | 0 |
| 5 | 4 | 3 | 1 | 2 | NA | 2 | 3 | 1 | 2 | 0 |
| 8 | 2 | 1 | 1 | 5 | 1 | 1 | 1 | 1 | 2 | 0 |
| 9 | 1 | 2 | 6 | 4 | 10 | 7 | 7 | 2 | 4 | 1 |
| 8 | 4 | 10 | 5 | 4 | 4 | 7 | 10 | 1 | 4 | 1 |
| 1 | 1 | 1 | 1 | 2 | 1 | 3 | 1 | 1 | 2 | 0 |
| 10 | 10 | 10 | 7 | 9 | 10 | 7 | 10 | 10 | 4 | 1 |
| 1 | 1 | 1 | 1 | 2 | 1 | 3 | 1 | 1 | 2 | 0 |
| 8 | 3 | 4 | 9 | 3 | 10 | 3 | 3 | 1 | 4 | 1 |
| 10 | 8 | 4 | 4 | 4 | 10 | 3 | 10 | 4 | 4 | 1 |
| 1 | 1 | 1 | 1 | 2 | 1 | 3 | 1 | 1 | 2 | 0 |
| 1 | 1 | 1 | 1 | 2 | 1 | 3 | 1 | 1 | 2 | 0 |
| 7 | 8 | 7 | 6 | 4 | 3 | 8 | 8 | 4 | 4 | 1 |
| 3 | 1 | 1 | 1 | 2 | 5 | 5 | 1 | 1 | 2 | 0 |
| 2 | 1 | 1 | 1 | 3 | 1 | 2 | 1 | 1 | 2 | 0 |
| 1 | 1 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 2 | 0 |
| 8 | 6 | 4 | 10 | 10 | 1 | 3 | 5 | 1 | 4 | 1 |
| 1 | 1 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 2 | 0 |
| 1 | 1 | 1 | 1 | 1 | 1 | 2 | 1 | 1 | 2 | 0 |
| 4 | 6 | 5 | 6 | 7 | NA | 4 | 9 | 1 | 2 | 0 |
| 5 | 5 | 5 | 2 | 5 | 10 | 4 | 3 | 1 | 4 | 1 |
| 6 | 8 | 7 | 8 | 6 | 8 | 8 | 9 | 1 | 4 | 1 |
| 1 | 1 | 1 | 1 | 5 | 1 | 3 | 1 | 1 | 2 | 0 |
| 4 | 4 | 4 | 4 | 6 | 5 | 7 | 3 | 1 | 2 | 0 |
| 7 | 6 | 3 | 2 | 5 | 10 | 7 | 4 | 6 | 4 | 1 |
| 3 | 1 | 1 | 1 | 2 | NA | 3 | 1 | 1 | 2 | 0 |
| 3 | 1 | 1 | 1 | 2 | 1 | 3 | 1 | 1 | 2 | 0 |
| 5 | 4 | 6 | 10 | 2 | 10 | 4 | 1 | 1 | 4 | 1 |
| 1 | 1 | 1 | 1 | 2 | 1 | 3 | 1 | 1 | 2 | 0 |
| 3 | 2 | 2 | 1 | 2 | 1 | 2 | 3 | 1 | 2 | 0 |
| 10 | 1 | 1 | 1 | 2 | 10 | 5 | 4 | 1 | 4 | 1 |
| 1 | 1 | 1 | 1 | 2 | 1 | 2 | 1 | 1 | 2 | 0 |
| 8 | 10 | 3 | 2 | 6 | 4 | 3 | 10 | 1 | 4 | 1 |
| 10 | 4 | 6 | 4 | 5 | 10 | 7 | 1 | 1 | 4 | 1 |
| 10 | 4 | 7 | 2 | 2 | 8 | 6 | 1 | 1 | 4 | 1 |
| 5 | 1 | 1 | 1 | 2 | 1 | 3 | 1 | 2 | 2 | 0 |
| 5 | 2 | 2 | 2 | 2 | 1 | 2 | 2 | 1 | 2 | 0 |
| 5 | 4 | 6 | 6 | 4 | 10 | 4 | 3 | 1 | 4 | 1 |
| 8 | 6 | 7 | 3 | 3 | 10 | 3 | 4 | 2 | 4 | 1 |
| 1 | 1 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 2 | 0 |
| 6 | 5 | 5 | 8 | 4 | 10 | 3 | 4 | 1 | 4 | 1 |
| 1 | 1 | 1 | 1 | 2 | 1 | 3 | 1 | 1 | 2 | 0 |
| 1 | 1 | 1 | 1 | 1 | 1 | 2 | 1 | 1 | 2 | 0 |
| 8 | 5 | 5 | 5 | 2 | 10 | 4 | 3 | 1 | 4 | 1 |
| 10 | 3 | 3 | 1 | 2 | 10 | 7 | 6 | 1 | 4 | 1 |
| 1 | 1 | 1 | 1 | 2 | 1 | 3 | 1 | 1 | 2 | 0 |
| 2 | 1 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 2 | 0 |
| 1 | 1 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 2 | 0 |
| 7 | 6 | 4 | 8 | 10 | 10 | 9 | 5 | 3 | 4 | 1 |
| 1 | 1 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 2 | 0 |
| 5 | 2 | 2 | 2 | 3 | 1 | 1 | 3 | 1 | 2 | 0 |
| 1 | 1 | 1 | 1 | 1 | 1 | 1 | 3 | 1 | 2 | 0 |
| 3 | 4 | 4 | 10 | 5 | 1 | 3 | 3 | 1 | 4 | 1 |
| 4 | 2 | 3 | 5 | 3 | 8 | 7 | 6 | 1 | 4 | 1 |
| 5 | 1 | 1 | 3 | 2 | 1 | 1 | 1 | 1 | 2 | 0 |
| 2 | 1 | 1 | 1 | 2 | 1 | 3 | 1 | 1 | 2 | 0 |
| 3 | 4 | 5 | 3 | 7 | 3 | 4 | 6 | 1 | 2 | 0 |
| 2 | 7 | 10 | 10 | 7 | 10 | 4 | 9 | 4 | 4 | 1 |
| 1 | 1 | 1 | 1 | 2 | 1 | 2 | 1 | 1 | 2 | 0 |
| 4 | 1 | 1 | 1 | 3 | 1 | 2 | 2 | 1 | 2 | 0 |
| 5 | 3 | 3 | 1 | 3 | 3 | 3 | 3 | 3 | 4 | 1 |
| 8 | 10 | 10 | 7 | 10 | 10 | 7 | 3 | 8 | 4 | 1 |
| 8 | 10 | 5 | 3 | 8 | 4 | 4 | 10 | 3 | 4 | 1 |
| 10 | 3 | 5 | 4 | 3 | 7 | 3 | 5 | 3 | 4 | 1 |
| 6 | 10 | 10 | 10 | 10 | 10 | 8 | 10 | 10 | 4 | 1 |
| 3 | 10 | 3 | 10 | 6 | 10 | 5 | 1 | 4 | 4 | 1 |
| 3 | 2 | 2 | 1 | 4 | 3 | 2 | 1 | 1 | 2 | 0 |
| 4 | 4 | 4 | 2 | 2 | 3 | 2 | 1 | 1 | 2 | 0 |
| 2 | 1 | 1 | 1 | 2 | 1 | 3 | 1 | 1 | 2 | 0 |
| 2 | 1 | 1 | 1 | 2 | 1 | 2 | 1 | 1 | 2 | 0 |
| 6 | 10 | 10 | 10 | 8 | 10 | 7 | 10 | 7 | 4 | 1 |
| 5 | 8 | 8 | 10 | 5 | 10 | 8 | 10 | 3 | 4 | 1 |
| 1 | 1 | 3 | 1 | 2 | 1 | 1 | 1 | 1 | 2 | 0 |
| 1 | 1 | 3 | 1 | 1 | 1 | 2 | 1 | 1 | 2 | 0 |
| 4 | 3 | 2 | 1 | 3 | 1 | 2 | 1 | 1 | 2 | 0 |
| 1 | 1 | 3 | 1 | 2 | 1 | 1 | 1 | 1 | 2 | 0 |
| 4 | 1 | 2 | 1 | 2 | 1 | 2 | 1 | 1 | 2 | 0 |
| 5 | 1 | 1 | 2 | 2 | 1 | 2 | 1 | 1 | 2 | 0 |
| 3 | 1 | 2 | 1 | 2 | 1 | 2 | 1 | 1 | 2 | 0 |
| 1 | 1 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 2 | 0 |
| 1 | 1 | 1 | 1 | 2 | 1 | 2 | 1 | 1 | 2 | 0 |
| 1 | 1 | 1 | 1 | 1 | 1 | 2 | 1 | 1 | 2 | 0 |
| 3 | 1 | 1 | 4 | 3 | 1 | 2 | 2 | 1 | 2 | 0 |
| 5 | 3 | 4 | 1 | 4 | 1 | 3 | 1 | 1 | 2 | 0 |
| 1 | 1 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 2 | 0 |
| 10 | 6 | 3 | 6 | 4 | 10 | 7 | 8 | 4 | 4 | 1 |
| 3 | 2 | 2 | 2 | 2 | 1 | 3 | 2 | 1 | 2 | 0 |
| 2 | 1 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 2 | 0 |
| 2 | 1 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 2 | 0 |
| 3 | 3 | 2 | 2 | 3 | 1 | 1 | 2 | 3 | 2 | 0 |
| 7 | 6 | 6 | 3 | 2 | 10 | 7 | 1 | 1 | 4 | 1 |
| 5 | 3 | 3 | 2 | 3 | 1 | 3 | 1 | 1 | 2 | 0 |
| 2 | 1 | 1 | 1 | 2 | 1 | 2 | 2 | 1 | 2 | 0 |
| 5 | 1 | 1 | 1 | 3 | 2 | 2 | 2 | 1 | 2 | 0 |
| 1 | 1 | 1 | 2 | 2 | 1 | 2 | 1 | 1 | 2 | 0 |
| 10 | 8 | 7 | 4 | 3 | 10 | 7 | 9 | 1 | 4 | 1 |
| 3 | 1 | 1 | 1 | 2 | 1 | 2 | 1 | 1 | 2 | 0 |
| 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 2 | 0 |
| 1 | 2 | 3 | 1 | 2 | 1 | 2 | 1 | 1 | 2 | 0 |
| 3 | 1 | 1 | 1 | 2 | 1 | 2 | 1 | 1 | 2 | 0 |
| 3 | 1 | 1 | 1 | 2 | 1 | 3 | 1 | 1 | 2 | 0 |
| 4 | 1 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 2 | 0 |
| 3 | 2 | 1 | 1 | 2 | 1 | 2 | 2 | 1 | 2 | 0 |
| 1 | 2 | 3 | 1 | 2 | 1 | 1 | 1 | 1 | 2 | 0 |
| 3 | 10 | 8 | 7 | 6 | 9 | 9 | 3 | 8 | 4 | 1 |
| 3 | 1 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 2 | 0 |
| 5 | 3 | 3 | 1 | 2 | 1 | 2 | 1 | 1 | 2 | 0 |
| 3 | 1 | 1 | 1 | 2 | 4 | 1 | 1 | 1 | 2 | 0 |
| 1 | 2 | 1 | 3 | 2 | 1 | 1 | 2 | 1 | 2 | 0 |
| 1 | 1 | 1 | 1 | 2 | 1 | 2 | 1 | 1 | 2 | 0 |
| 4 | 2 | 2 | 1 | 2 | 1 | 2 | 1 | 1 | 2 | 0 |
| 1 | 1 | 1 | 1 | 2 | 1 | 2 | 1 | 1 | 2 | 0 |
| 2 | 3 | 2 | 2 | 2 | 2 | 3 | 1 | 1 | 2 | 0 |
| 3 | 1 | 2 | 1 | 2 | 1 | 2 | 1 | 1 | 2 | 0 |
| 1 | 1 | 1 | 1 | 2 | 1 | 2 | 1 | 1 | 2 | 0 |
| 1 | 1 | 1 | 1 | 1 | NA | 2 | 1 | 1 | 2 | 0 |
| 10 | 10 | 10 | 6 | 8 | 4 | 8 | 5 | 1 | 4 | 1 |
| 5 | 1 | 2 | 1 | 2 | 1 | 3 | 1 | 1 | 2 | 0 |
| 8 | 5 | 6 | 2 | 3 | 10 | 6 | 6 | 1 | 4 | 1 |
| 3 | 3 | 2 | 6 | 3 | 3 | 3 | 5 | 1 | 2 | 0 |
| 8 | 7 | 8 | 5 | 10 | 10 | 7 | 2 | 1 | 4 | 1 |
| 1 | 1 | 1 | 1 | 2 | 1 | 2 | 1 | 1 | 2 | 0 |
| 5 | 2 | 2 | 2 | 2 | 2 | 3 | 2 | 2 | 2 | 0 |
| 2 | 3 | 1 | 1 | 5 | 1 | 1 | 1 | 1 | 2 | 0 |
| 3 | 2 | 2 | 3 | 2 | 3 | 3 | 1 | 1 | 2 | 0 |
| 10 | 10 | 10 | 7 | 10 | 10 | 8 | 2 | 1 | 4 | 1 |
| 4 | 3 | 3 | 1 | 2 | 1 | 3 | 3 | 1 | 2 | 0 |
| 5 | 1 | 3 | 1 | 2 | 1 | 2 | 1 | 1 | 2 | 0 |
| 3 | 1 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 2 | 0 |
| 9 | 10 | 10 | 10 | 10 | 10 | 10 | 10 | 1 | 4 | 1 |
| 5 | 3 | 6 | 1 | 2 | 1 | 1 | 1 | 1 | 2 | 0 |
| 8 | 7 | 8 | 2 | 4 | 2 | 5 | 10 | 1 | 4 | 1 |
| 1 | 1 | 1 | 1 | 2 | 1 | 2 | 1 | 1 | 2 | 0 |
| 2 | 1 | 1 | 1 | 2 | 1 | 2 | 1 | 1 | 2 | 0 |
| 1 | 3 | 1 | 1 | 2 | 1 | 2 | 2 | 1 | 2 | 0 |
| 5 | 1 | 1 | 3 | 4 | 1 | 3 | 2 | 1 | 2 | 0 |
| 5 | 1 | 1 | 1 | 2 | 1 | 2 | 2 | 1 | 2 | 0 |
| 3 | 2 | 2 | 3 | 2 | 1 | 1 | 1 | 1 | 2 | 0 |
| 6 | 9 | 7 | 5 | 5 | 8 | 4 | 2 | 1 | 2 | 0 |
| 10 | 8 | 10 | 1 | 3 | 10 | 5 | 1 | 1 | 4 | 1 |
| 10 | 10 | 10 | 1 | 6 | 1 | 2 | 8 | 1 | 4 | 1 |
| 4 | 1 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 2 | 0 |
| 4 | 1 | 3 | 3 | 2 | 1 | 1 | 1 | 1 | 2 | 0 |
| 5 | 1 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 2 | 0 |
| 10 | 4 | 3 | 10 | 4 | 10 | 10 | 1 | 1 | 4 | 1 |
| 5 | 2 | 2 | 4 | 2 | 4 | 1 | 1 | 1 | 2 | 0 |
| 1 | 1 | 1 | 3 | 2 | 3 | 1 | 1 | 1 | 2 | 0 |
| 1 | 1 | 1 | 1 | 2 | 2 | 1 | 1 | 1 | 2 | 0 |
| 5 | 1 | 1 | 6 | 3 | 1 | 2 | 1 | 1 | 2 | 0 |
| 2 | 1 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 2 | 0 |
| 1 | 1 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 2 | 0 |
| 5 | 1 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 2 | 0 |
| 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 2 | 0 |
| 5 | 7 | 9 | 8 | 6 | 10 | 8 | 10 | 1 | 4 | 1 |
| 4 | 1 | 1 | 3 | 1 | 1 | 2 | 1 | 1 | 2 | 0 |
| 5 | 1 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 2 | 0 |
| 3 | 1 | 1 | 3 | 2 | 1 | 1 | 1 | 1 | 2 | 0 |
| 4 | 5 | 5 | 8 | 6 | 10 | 10 | 7 | 1 | 4 | 1 |
| 2 | 3 | 1 | 1 | 3 | 1 | 1 | 1 | 1 | 2 | 0 |
| 10 | 2 | 2 | 1 | 2 | 6 | 1 | 1 | 2 | 4 | 1 |
| 10 | 6 | 5 | 8 | 5 | 10 | 8 | 6 | 1 | 4 | 1 |
| 8 | 8 | 9 | 6 | 6 | 3 | 10 | 10 | 1 | 4 | 1 |
| 5 | 1 | 2 | 1 | 2 | 1 | 1 | 1 | 1 | 2 | 0 |
| 5 | 1 | 3 | 1 | 2 | 1 | 1 | 1 | 1 | 2 | 0 |
| 5 | 1 | 1 | 3 | 2 | 1 | 1 | 1 | 1 | 2 | 0 |
| 3 | 1 | 1 | 1 | 2 | 5 | 1 | 1 | 1 | 2 | 0 |
| 6 | 1 | 1 | 3 | 2 | 1 | 1 | 1 | 1 | 2 | 0 |
| 4 | 1 | 1 | 1 | 2 | 1 | 1 | 2 | 1 | 2 | 0 |
| 4 | 1 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 2 | 0 |
| 10 | 9 | 8 | 7 | 6 | 4 | 7 | 10 | 3 | 4 | 1 |
| 10 | 6 | 6 | 2 | 4 | 10 | 9 | 7 | 1 | 4 | 1 |
| 6 | 6 | 6 | 5 | 4 | 10 | 7 | 6 | 2 | 4 | 1 |
| 4 | 1 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 2 | 0 |
| 1 | 1 | 2 | 1 | 2 | 1 | 2 | 1 | 1 | 2 | 0 |
| 3 | 1 | 1 | 1 | 1 | 1 | 2 | 1 | 1 | 2 | 0 |
| 6 | 1 | 1 | 3 | 2 | 1 | 1 | 1 | 1 | 2 | 0 |
| 6 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 2 | 0 |
| 4 | 1 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 2 | 0 |
| 5 | 1 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 2 | 0 |
| 3 | 1 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 2 | 0 |
| 4 | 1 | 2 | 1 | 2 | 1 | 1 | 1 | 1 | 2 | 0 |
| 4 | 1 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 2 | 0 |
| 5 | 2 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 2 | 0 |
| 4 | 8 | 7 | 10 | 4 | 10 | 7 | 5 | 1 | 4 | 1 |
| 5 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 2 | 0 |
| 5 | 3 | 2 | 4 | 2 | 1 | 1 | 1 | 1 | 2 | 0 |
| 9 | 10 | 10 | 10 | 10 | 5 | 10 | 10 | 10 | 4 | 1 |
| 8 | 7 | 8 | 5 | 5 | 10 | 9 | 10 | 1 | 4 | 1 |
| 5 | 1 | 2 | 1 | 2 | 1 | 1 | 1 | 1 | 2 | 0 |
| 1 | 1 | 1 | 3 | 1 | 3 | 1 | 1 | 1 | 2 | 0 |
| 3 | 1 | 1 | 1 | 1 | 1 | 2 | 1 | 1 | 2 | 0 |
| 10 | 10 | 10 | 10 | 6 | 10 | 8 | 1 | 5 | 4 | 1 |
| 3 | 6 | 4 | 10 | 3 | 3 | 3 | 4 | 1 | 4 | 1 |
| 6 | 3 | 2 | 1 | 3 | 4 | 4 | 1 | 1 | 4 | 1 |
| 1 | 1 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 2 | 0 |
| 5 | 8 | 9 | 4 | 3 | 10 | 7 | 1 | 1 | 4 | 1 |
| 4 | 1 | 1 | 1 | 1 | 1 | 2 | 1 | 1 | 2 | 0 |
| 5 | 10 | 10 | 10 | 6 | 10 | 6 | 5 | 2 | 4 | 1 |
| 5 | 1 | 2 | 10 | 4 | 5 | 2 | 1 | 1 | 2 | 0 |
| 3 | 1 | 1 | 1 | 1 | 1 | 2 | 1 | 1 | 2 | 0 |
| 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 2 | 0 |
| 4 | 2 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 2 | 0 |
| 4 | 1 | 1 | 1 | 2 | 1 | 2 | 1 | 1 | 2 | 0 |
| 4 | 1 | 1 | 1 | 2 | 1 | 2 | 1 | 1 | 2 | 0 |
| 6 | 1 | 1 | 1 | 2 | 1 | 3 | 1 | 1 | 2 | 0 |
| 4 | 1 | 1 | 1 | 2 | 1 | 2 | 1 | 1 | 2 | 0 |
| 4 | 1 | 1 | 2 | 2 | 1 | 2 | 1 | 1 | 2 | 0 |
| 4 | 1 | 1 | 1 | 2 | 1 | 3 | 1 | 1 | 2 | 0 |
| 1 | 1 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 2 | 0 |
| 3 | 3 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 2 | 0 |
| 8 | 10 | 10 | 10 | 7 | 5 | 4 | 8 | 7 | 4 | 1 |
| 1 | 1 | 1 | 1 | 2 | 4 | 1 | 1 | 1 | 2 | 0 |
| 5 | 1 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 2 | 0 |
| 2 | 1 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 2 | 0 |
| 1 | 1 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 2 | 0 |
| 5 | 1 | 1 | 1 | 2 | 1 | 2 | 1 | 1 | 2 | 0 |
| 5 | 1 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 2 | 0 |
| 3 | 1 | 1 | 1 | 1 | 1 | 2 | 1 | 1 | 2 | 0 |
| 6 | 6 | 7 | 10 | 3 | 10 | 8 | 10 | 2 | 4 | 1 |
| 4 | 10 | 4 | 7 | 3 | 10 | 9 | 10 | 1 | 4 | 1 |
| 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 2 | 0 |
| 1 | 1 | 1 | 1 | 1 | 1 | 2 | 1 | 1 | 2 | 0 |
| 3 | 1 | 2 | 2 | 2 | 1 | 1 | 1 | 1 | 2 | 0 |
| 4 | 7 | 8 | 3 | 4 | 10 | 9 | 1 | 1 | 4 | 1 |
| 1 | 1 | 1 | 1 | 3 | 1 | 1 | 1 | 1 | 2 | 0 |
| 4 | 1 | 1 | 1 | 3 | 1 | 1 | 1 | 1 | 2 | 0 |
| 10 | 4 | 5 | 4 | 3 | 5 | 7 | 3 | 1 | 4 | 1 |
| 7 | 5 | 6 | 10 | 4 | 10 | 5 | 3 | 1 | 4 | 1 |
| 3 | 1 | 1 | 1 | 2 | 1 | 2 | 1 | 1 | 2 | 0 |
| 3 | 1 | 1 | 2 | 2 | 1 | 1 | 1 | 1 | 2 | 0 |
| 4 | 1 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 2 | 0 |
| 4 | 1 | 1 | 1 | 2 | 1 | 3 | 1 | 1 | 2 | 0 |
| 6 | 1 | 3 | 2 | 2 | 1 | 1 | 1 | 1 | 2 | 0 |
| 4 | 1 | 1 | 1 | 1 | 1 | 2 | 1 | 1 | 2 | 0 |
| 7 | 4 | 4 | 3 | 4 | 10 | 6 | 9 | 1 | 4 | 1 |
| 4 | 2 | 2 | 1 | 2 | 1 | 2 | 1 | 1 | 2 | 0 |
| 1 | 1 | 1 | 1 | 1 | 1 | 3 | 1 | 1 | 2 | 0 |
| 3 | 1 | 1 | 1 | 2 | 1 | 2 | 1 | 1 | 2 | 0 |
| 2 | 1 | 1 | 1 | 2 | 1 | 2 | 1 | 1 | 2 | 0 |
| 1 | 1 | 3 | 2 | 2 | 1 | 3 | 1 | 1 | 2 | 0 |
| 5 | 1 | 1 | 1 | 2 | 1 | 3 | 1 | 1 | 2 | 0 |
| 5 | 1 | 2 | 1 | 2 | 1 | 3 | 1 | 1 | 2 | 0 |
| 4 | 1 | 1 | 1 | 2 | 1 | 2 | 1 | 1 | 2 | 0 |
| 6 | 1 | 1 | 1 | 2 | 1 | 2 | 1 | 1 | 2 | 0 |
| 5 | 1 | 1 | 1 | 2 | 2 | 2 | 1 | 1 | 2 | 0 |
| 3 | 1 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 2 | 0 |
| 5 | 3 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 2 | 0 |
| 4 | 1 | 1 | 1 | 2 | 1 | 2 | 1 | 1 | 2 | 0 |
| 2 | 1 | 3 | 2 | 2 | 1 | 2 | 1 | 1 | 2 | 0 |
| 5 | 1 | 1 | 1 | 2 | 1 | 2 | 1 | 1 | 2 | 0 |
| 6 | 10 | 10 | 10 | 4 | 10 | 7 | 10 | 1 | 4 | 1 |
| 2 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 2 | 0 |
| 3 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 2 | 0 |
| 7 | 8 | 3 | 7 | 4 | 5 | 7 | 8 | 2 | 4 | 1 |
| 3 | 1 | 1 | 1 | 2 | 1 | 2 | 1 | 1 | 2 | 0 |
| 1 | 1 | 1 | 1 | 2 | 1 | 3 | 1 | 1 | 2 | 0 |
| 3 | 2 | 2 | 2 | 2 | 1 | 4 | 2 | 1 | 2 | 0 |
| 4 | 4 | 2 | 1 | 2 | 5 | 2 | 1 | 2 | 2 | 0 |
| 3 | 1 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 2 | 0 |
| 4 | 3 | 1 | 1 | 2 | 1 | 4 | 8 | 1 | 2 | 0 |
| 5 | 2 | 2 | 2 | 1 | 1 | 2 | 1 | 1 | 2 | 0 |
| 5 | 1 | 1 | 3 | 2 | 1 | 1 | 1 | 1 | 2 | 0 |
| 2 | 1 | 1 | 1 | 2 | 1 | 2 | 1 | 1 | 2 | 0 |
| 5 | 1 | 1 | 1 | 2 | 1 | 2 | 1 | 1 | 2 | 0 |
| 5 | 1 | 1 | 1 | 2 | 1 | 3 | 1 | 1 | 2 | 0 |
| 5 | 1 | 1 | 1 | 2 | 1 | 3 | 1 | 1 | 2 | 0 |
| 1 | 1 | 1 | 1 | 2 | 1 | 3 | 1 | 1 | 2 | 0 |
| 3 | 1 | 1 | 1 | 2 | 1 | 2 | 1 | 1 | 2 | 0 |
| 4 | 1 | 1 | 1 | 2 | 1 | 3 | 2 | 1 | 2 | 0 |
| 5 | 7 | 10 | 10 | 5 | 10 | 10 | 10 | 1 | 4 | 1 |
| 3 | 1 | 2 | 1 | 2 | 1 | 3 | 1 | 1 | 2 | 0 |
| 4 | 1 | 1 | 1 | 2 | 3 | 2 | 1 | 1 | 2 | 0 |
| 8 | 4 | 4 | 1 | 6 | 10 | 2 | 5 | 2 | 4 | 1 |
| 10 | 10 | 8 | 10 | 6 | 5 | 10 | 3 | 1 | 4 | 1 |
| 8 | 10 | 4 | 4 | 8 | 10 | 8 | 2 | 1 | 4 | 1 |
| 7 | 6 | 10 | 5 | 3 | 10 | 9 | 10 | 2 | 4 | 1 |
| 3 | 1 | 1 | 1 | 2 | 1 | 2 | 1 | 1 | 2 | 0 |
| 1 | 1 | 1 | 1 | 2 | 1 | 2 | 1 | 1 | 2 | 0 |
| 10 | 9 | 7 | 3 | 4 | 2 | 7 | 7 | 1 | 4 | 1 |
| 5 | 1 | 2 | 1 | 2 | 1 | 3 | 1 | 1 | 2 | 0 |
| 5 | 1 | 1 | 1 | 2 | 1 | 2 | 1 | 1 | 2 | 0 |
| 1 | 1 | 1 | 1 | 2 | 1 | 2 | 1 | 1 | 2 | 0 |
| 1 | 1 | 1 | 1 | 2 | 1 | 2 | 1 | 1 | 2 | 0 |
| 1 | 1 | 1 | 1 | 2 | 1 | 3 | 1 | 1 | 2 | 0 |
| 5 | 1 | 2 | 1 | 2 | 1 | 2 | 1 | 1 | 2 | 0 |
| 5 | 7 | 10 | 6 | 5 | 10 | 7 | 5 | 1 | 4 | 1 |
| 6 | 10 | 5 | 5 | 4 | 10 | 6 | 10 | 1 | 4 | 1 |
| 3 | 1 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 2 | 0 |
| 5 | 1 | 1 | 6 | 3 | 1 | 1 | 1 | 1 | 2 | 0 |
| 1 | 1 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 2 | 0 |
| 8 | 10 | 10 | 10 | 6 | 10 | 10 | 10 | 1 | 4 | 1 |
| 5 | 1 | 1 | 1 | 2 | 1 | 2 | 2 | 1 | 2 | 0 |
| 9 | 8 | 8 | 9 | 6 | 3 | 4 | 1 | 1 | 4 | 1 |
| 5 | 1 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 2 | 0 |
| 4 | 10 | 8 | 5 | 4 | 1 | 10 | 1 | 1 | 4 | 1 |
| 2 | 5 | 7 | 6 | 4 | 10 | 7 | 6 | 1 | 4 | 1 |
| 10 | 3 | 4 | 5 | 3 | 10 | 4 | 1 | 1 | 4 | 1 |
| 5 | 1 | 2 | 1 | 2 | 1 | 1 | 1 | 1 | 2 | 0 |
| 4 | 8 | 6 | 3 | 4 | 10 | 7 | 1 | 1 | 4 | 1 |
| 5 | 1 | 1 | 1 | 2 | 1 | 2 | 1 | 1 | 2 | 0 |
| 4 | 1 | 2 | 1 | 2 | 1 | 2 | 1 | 1 | 2 | 0 |
| 5 | 1 | 3 | 1 | 2 | 1 | 3 | 1 | 1 | 2 | 0 |
| 3 | 1 | 1 | 1 | 2 | 1 | 2 | 1 | 1 | 2 | 0 |
| 5 | 2 | 4 | 1 | 1 | 1 | 1 | 1 | 1 | 2 | 0 |
| 3 | 1 | 1 | 1 | 2 | 1 | 2 | 1 | 1 | 2 | 0 |
| 1 | 1 | 1 | 1 | 1 | 1 | 2 | 1 | 1 | 2 | 0 |
| 4 | 1 | 1 | 1 | 2 | 1 | 2 | 1 | 1 | 2 | 0 |
| 5 | 4 | 6 | 8 | 4 | 1 | 8 | 10 | 1 | 4 | 1 |
| 5 | 3 | 2 | 8 | 5 | 10 | 8 | 1 | 2 | 4 | 1 |
| 10 | 5 | 10 | 3 | 5 | 8 | 7 | 8 | 3 | 4 | 1 |
| 4 | 1 | 1 | 2 | 2 | 1 | 1 | 1 | 1 | 2 | 0 |
| 1 | 1 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 2 | 0 |
| 5 | 10 | 10 | 10 | 10 | 10 | 10 | 1 | 1 | 4 | 1 |
| 5 | 1 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 2 | 0 |
| 10 | 4 | 3 | 10 | 3 | 10 | 7 | 1 | 2 | 4 | 1 |
| 5 | 10 | 10 | 10 | 5 | 2 | 8 | 5 | 1 | 4 | 1 |
| 8 | 10 | 10 | 10 | 6 | 10 | 10 | 10 | 10 | 4 | 1 |
| 2 | 3 | 1 | 1 | 2 | 1 | 2 | 1 | 1 | 2 | 0 |
| 2 | 1 | 1 | 1 | 1 | 1 | 2 | 1 | 1 | 2 | 0 |
| 4 | 1 | 3 | 1 | 2 | 1 | 2 | 1 | 1 | 2 | 0 |
| 3 | 1 | 1 | 1 | 2 | 1 | 2 | 1 | 1 | 2 | 0 |
| 1 | 1 | 1 | 1 | 1 | NA | 1 | 1 | 1 | 2 | 0 |
| 4 | 1 | 1 | 1 | 2 | 1 | 2 | 1 | 1 | 2 | 0 |
| 5 | 1 | 1 | 1 | 2 | 1 | 2 | 1 | 1 | 2 | 0 |
| 3 | 1 | 1 | 1 | 2 | 1 | 2 | 1 | 1 | 2 | 0 |
| 6 | 3 | 3 | 3 | 3 | 2 | 6 | 1 | 1 | 2 | 0 |
| 7 | 1 | 2 | 3 | 2 | 1 | 2 | 1 | 1 | 2 | 0 |
| 1 | 1 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 2 | 0 |
| 5 | 1 | 1 | 2 | 1 | 1 | 2 | 1 | 1 | 2 | 0 |
| 3 | 1 | 3 | 1 | 3 | 4 | 1 | 1 | 1 | 2 | 0 |
| 4 | 6 | 6 | 5 | 7 | 6 | 7 | 7 | 3 | 4 | 1 |
| 2 | 1 | 1 | 1 | 2 | 5 | 1 | 1 | 1 | 2 | 0 |
| 2 | 1 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 2 | 0 |
| 4 | 1 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 2 | 0 |
| 6 | 2 | 3 | 1 | 2 | 1 | 1 | 1 | 1 | 2 | 0 |
| 5 | 1 | 1 | 1 | 2 | 1 | 2 | 1 | 1 | 2 | 0 |
| 1 | 1 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 2 | 0 |
| 8 | 7 | 4 | 4 | 5 | 3 | 5 | 10 | 1 | 4 | 1 |
| 3 | 1 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 2 | 0 |
| 3 | 1 | 4 | 1 | 2 | 1 | 1 | 1 | 1 | 2 | 0 |
| 10 | 10 | 7 | 8 | 7 | 1 | 10 | 10 | 3 | 4 | 1 |
| 4 | 2 | 4 | 3 | 2 | 2 | 2 | 1 | 1 | 2 | 0 |
| 4 | 1 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 2 | 0 |
| 5 | 1 | 1 | 3 | 2 | 1 | 1 | 1 | 1 | 2 | 0 |
| 4 | 1 | 1 | 3 | 2 | 1 | 1 | 1 | 1 | 2 | 0 |
| 3 | 1 | 1 | 1 | 2 | 1 | 2 | 1 | 1 | 2 | 0 |
| 3 | 1 | 1 | 1 | 2 | 1 | 2 | 1 | 1 | 2 | 0 |
| 1 | 1 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 2 | 0 |
| 2 | 1 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 2 | 0 |
| 3 | 1 | 1 | 1 | 2 | 1 | 2 | 1 | 1 | 2 | 0 |
| 1 | 2 | 2 | 1 | 2 | 1 | 1 | 1 | 1 | 2 | 0 |
| 1 | 1 | 1 | 3 | 2 | 1 | 1 | 1 | 1 | 2 | 0 |
| 5 | 10 | 10 | 10 | 10 | 2 | 10 | 10 | 10 | 4 | 1 |
| 3 | 1 | 1 | 1 | 2 | 1 | 2 | 1 | 1 | 2 | 0 |
| 3 | 1 | 1 | 2 | 3 | 4 | 1 | 1 | 1 | 2 | 0 |
| 1 | 2 | 1 | 3 | 2 | 1 | 2 | 1 | 1 | 2 | 0 |
| 5 | 1 | 1 | 1 | 2 | 1 | 2 | 2 | 1 | 2 | 0 |
| 4 | 1 | 1 | 1 | 2 | 1 | 2 | 1 | 1 | 2 | 0 |
| 3 | 1 | 1 | 1 | 2 | 1 | 3 | 1 | 1 | 2 | 0 |
| 3 | 1 | 1 | 1 | 2 | 1 | 2 | 1 | 1 | 2 | 0 |
| 5 | 1 | 1 | 1 | 2 | 1 | 2 | 1 | 1 | 2 | 0 |
| 5 | 4 | 5 | 1 | 8 | 1 | 3 | 6 | 1 | 2 | 0 |
| 7 | 8 | 8 | 7 | 3 | 10 | 7 | 2 | 3 | 4 | 1 |
| 1 | 1 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 2 | 0 |
| 1 | 1 | 1 | 1 | 2 | 1 | 2 | 1 | 1 | 2 | 0 |
| 4 | 1 | 1 | 1 | 2 | 1 | 3 | 1 | 1 | 2 | 0 |
| 1 | 1 | 3 | 1 | 2 | 1 | 2 | 1 | 1 | 2 | 0 |
| 1 | 1 | 3 | 1 | 2 | 1 | 2 | 1 | 1 | 2 | 0 |
| 3 | 1 | 1 | 3 | 2 | 1 | 2 | 1 | 1 | 2 | 0 |
| 1 | 1 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 2 | 0 |
| 5 | 2 | 2 | 2 | 2 | 1 | 1 | 1 | 2 | 2 | 0 |
| 3 | 1 | 1 | 1 | 2 | 1 | 3 | 1 | 1 | 2 | 0 |
| 5 | 7 | 4 | 1 | 6 | 1 | 7 | 10 | 3 | 4 | 1 |
| 5 | 10 | 10 | 8 | 5 | 5 | 7 | 10 | 1 | 4 | 1 |
| 3 | 10 | 7 | 8 | 5 | 8 | 7 | 4 | 1 | 4 | 1 |
| 3 | 2 | 1 | 2 | 2 | 1 | 3 | 1 | 1 | 2 | 0 |
| 2 | 1 | 1 | 1 | 2 | 1 | 3 | 1 | 1 | 2 | 0 |
| 5 | 3 | 2 | 1 | 3 | 1 | 1 | 1 | 1 | 2 | 0 |
| 1 | 1 | 1 | 1 | 2 | 1 | 2 | 1 | 1 | 2 | 0 |
| 4 | 1 | 4 | 1 | 2 | 1 | 1 | 1 | 1 | 2 | 0 |
| 1 | 1 | 2 | 1 | 2 | 1 | 2 | 1 | 1 | 2 | 0 |
| 5 | 1 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 2 | 0 |
| 1 | 1 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 2 | 0 |
| 2 | 1 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 2 | 0 |
| 10 | 10 | 10 | 10 | 5 | 10 | 10 | 10 | 7 | 4 | 1 |
| 5 | 10 | 10 | 10 | 4 | 10 | 5 | 6 | 3 | 4 | 1 |
| 5 | 1 | 1 | 1 | 2 | 1 | 3 | 2 | 1 | 2 | 0 |
| 1 | 1 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 2 | 0 |
| 1 | 1 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 2 | 0 |
| 1 | 1 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 2 | 0 |
| 1 | 1 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 2 | 0 |
| 3 | 1 | 1 | 1 | 2 | 1 | 2 | 3 | 1 | 2 | 0 |
| 4 | 1 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 2 | 0 |
| 1 | 1 | 1 | 1 | 2 | 1 | 1 | 1 | 8 | 2 | 0 |
| 1 | 1 | 1 | 3 | 2 | 1 | 1 | 1 | 1 | 2 | 0 |
| 5 | 10 | 10 | 5 | 4 | 5 | 4 | 4 | 1 | 4 | 1 |
| 3 | 1 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 2 | 0 |
| 3 | 1 | 1 | 1 | 2 | 1 | 2 | 1 | 2 | 2 | 0 |
| 3 | 1 | 1 | 1 | 3 | 2 | 1 | 1 | 1 | 2 | 0 |
| 2 | 1 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 2 | 0 |
| 5 | 10 | 10 | 3 | 7 | 3 | 8 | 10 | 2 | 4 | 1 |
| 4 | 8 | 6 | 4 | 3 | 4 | 10 | 6 | 1 | 4 | 1 |
| 4 | 8 | 8 | 5 | 4 | 5 | 10 | 4 | 1 | 4 | 1 |
Multivariate Data Analysis
There are 16 missing valus in Bare Nuclei predictor. The most of these appear in 0 response variable (14 observations) and we have 458 cases of 0 category and 241 of 1 category, thus I can remove these observations from dataset.
data2 = data[!is.na(data$BN),]Basic Stats
data2$ClassCat[data2$Dia == 4] = 'Malignant'
data2$ClassCat[data2$Dia == 2] = 'Benign'Stats for category of Malignant:
malignant = summarytools::descr(data2[,c(1:9, 12)] %>% filter(ClassCat == "Malignant") %>% select(-ClassCat), round.digits=3)
kbl(malignant) %>%
kable_paper() %>%
scroll_box(width = "800px", height = "300px")| BC | BN | CT | ECSize | M | MAF | NN | UCShape | UCSize | |
|---|---|---|---|---|---|---|---|---|---|
| Mean | 5.9748954 | 7.6276151 | 7.1882845 | 5.3263598 | 2.6025105 | 5.5857741 | 5.8577406 | 6.5606695 | 6.5774059 |
| Std.Dev | 2.2824222 | 3.1166790 | 2.4379072 | 2.4430866 | 2.5644946 | 3.1966314 | 3.3488761 | 2.5691040 | 2.7242438 |
| Min | 1.0000000 | 1.0000000 | 1.0000000 | 1.0000000 | 1.0000000 | 1.0000000 | 1.0000000 | 1.0000000 | 1.0000000 |
| Q1 | 4.0000000 | 5.0000000 | 5.0000000 | 3.0000000 | 1.0000000 | 3.0000000 | 3.0000000 | 4.0000000 | 4.0000000 |
| Median | 7.0000000 | 10.0000000 | 8.0000000 | 5.0000000 | 1.0000000 | 5.0000000 | 6.0000000 | 6.0000000 | 6.0000000 |
| Q3 | 7.0000000 | 10.0000000 | 10.0000000 | 7.0000000 | 3.0000000 | 8.0000000 | 10.0000000 | 9.0000000 | 10.0000000 |
| Max | 10.0000000 | 10.0000000 | 10.0000000 | 10.0000000 | 10.0000000 | 10.0000000 | 10.0000000 | 10.0000000 | 10.0000000 |
| MAD | 2.9652000 | 0.0000000 | 2.9652000 | 2.9652000 | 0.0000000 | 4.4478000 | 4.4478000 | 2.9652000 | 2.9652000 |
| IQR | 3.0000000 | 5.0000000 | 5.0000000 | 3.5000000 | 2.0000000 | 5.0000000 | 6.5000000 | 5.0000000 | 6.0000000 |
| CV | 0.3820020 | 0.4086047 | 0.3391501 | 0.4586785 | 0.9853926 | 0.5722808 | 0.5717010 | 0.3915917 | 0.4141821 |
| Skewness | 0.0011900 | -0.9140862 | -0.4053150 | 0.5987979 | 1.7664759 | 0.0960912 | -0.1047398 | -0.0356159 | -0.0839503 |
| SE.Skewness | 0.1574619 | 0.1574619 | 0.1574619 | 0.1574619 | 0.1574619 | 0.1574619 | 0.1574619 | 0.1574619 | 0.1574619 |
| Kurtosis | -0.9926759 | -0.7055967 | -0.8940947 | -0.6469011 | 2.0634855 | -1.3942210 | -1.4693864 | -1.2109414 | -1.2932736 |
| N.Valid | 239.0000000 | 239.0000000 | 239.0000000 | 239.0000000 | 239.0000000 | 239.0000000 | 239.0000000 | 239.0000000 | 239.0000000 |
| Pct.Valid | 100.0000000 | 100.0000000 | 100.0000000 | 100.0000000 | 100.0000000 | 100.0000000 | 100.0000000 | 100.0000000 | 100.0000000 |
Stats for category of Benign:
benign = summarytools::descr(data2[,c(1:9, 12)] %>% filter(ClassCat == "Benign") %>% select(-ClassCat), round.digits=3)
kbl(benign) %>%
kable_paper() %>%
scroll_box(width = "800px", height = "300px")| BC | BN | CT | ECSize | M | MAF | NN | UCShape | UCSize | |
|---|---|---|---|---|---|---|---|---|---|
| Mean | 2.0833333 | 1.3468468 | 2.9639640 | 2.1081081 | 1.0653153 | 1.3468468 | 1.2612613 | 1.4144144 | 1.3063063 |
| Std.Dev | 1.0622994 | 1.1778482 | 1.6726612 | 0.8771115 | 0.5097378 | 0.9170883 | 0.9546059 | 0.9570314 | 0.8556575 |
| Min | 1.0000000 | 1.0000000 | 1.0000000 | 1.0000000 | 1.0000000 | 1.0000000 | 1.0000000 | 1.0000000 | 1.0000000 |
| Q1 | 1.0000000 | 1.0000000 | 1.0000000 | 2.0000000 | 1.0000000 | 1.0000000 | 1.0000000 | 1.0000000 | 1.0000000 |
| Median | 2.0000000 | 1.0000000 | 3.0000000 | 2.0000000 | 1.0000000 | 1.0000000 | 1.0000000 | 1.0000000 | 1.0000000 |
| Q3 | 3.0000000 | 1.0000000 | 4.0000000 | 2.0000000 | 1.0000000 | 1.0000000 | 1.0000000 | 1.0000000 | 1.0000000 |
| Max | 7.0000000 | 10.0000000 | 8.0000000 | 10.0000000 | 8.0000000 | 10.0000000 | 8.0000000 | 8.0000000 | 9.0000000 |
| MAD | 1.4826000 | 0.0000000 | 2.9652000 | 0.0000000 | 0.0000000 | 0.0000000 | 0.0000000 | 0.0000000 | 0.0000000 |
| IQR | 2.0000000 | 0.0000000 | 3.0000000 | 0.0000000 | 0.0000000 | 0.0000000 | 0.0000000 | 0.0000000 | 0.0000000 |
| CV | 0.5099037 | 0.8745227 | 0.5643325 | 0.4160657 | 0.4784854 | 0.6809151 | 0.7568661 | 0.6766273 | 0.6550206 |
| Skewness | 1.5589448 | 4.6393761 | 0.3368983 | 4.4155162 | 10.6165880 | 4.0114527 | 4.9442885 | 3.1654389 | 4.4191442 |
| SE.Skewness | 0.1158575 | 0.1158575 | 0.1158575 | 0.1158575 | 0.1158575 | 0.1158575 | 0.1158575 | 0.1158575 | 0.1158575 |
| Kurtosis | 4.8128913 | 25.0286648 | -0.7904512 | 28.1507180 | 124.6675949 | 23.1618256 | 26.6467237 | 12.5306254 | 27.3250711 |
| N.Valid | 444.0000000 | 444.0000000 | 444.0000000 | 444.0000000 | 444.0000000 | 444.0000000 | 444.0000000 | 444.0000000 | 444.0000000 |
| Pct.Valid | 100.0000000 | 100.0000000 | 100.0000000 | 100.0000000 | 100.0000000 | 100.0000000 | 100.0000000 | 100.0000000 | 100.0000000 |
Correlation Chart
pairs.panels(data[,c(1:9)], method="pearson",
hist.col = "#1fbbfa", density=TRUE, ellipses=TRUE, show.points = TRUE,
pch=1, lm=TRUE, cex.cor=1, smoother=F, stars = T, main="Correlation chart for all potentional predictors")Principal Component Analysis
all_pca = prcomp(data2[,1:9], cor=TRUE, scale = TRUE)
summary(all_pca)## Importance of components:
## PC1 PC2 PC3 PC4 PC5 PC6 PC7
## Standard deviation 2.4289 0.88088 0.73434 0.67796 0.61667 0.54943 0.54259
## Proportion of Variance 0.6555 0.08622 0.05992 0.05107 0.04225 0.03354 0.03271
## Cumulative Proportion 0.6555 0.74172 0.80163 0.85270 0.89496 0.92850 0.96121
## PC8 PC9
## Standard deviation 0.51062 0.29729
## Proportion of Variance 0.02897 0.00982
## Cumulative Proportion 0.99018 1.00000
kbl(all_pca$rotation[,1:5]) %>%
kable_paper()| PC1 | PC2 | PC3 | PC4 | PC5 | |
|---|---|---|---|---|---|
| CT | -0.3020626 | -0.1408005 | 0.8663725 | -0.1078284 | 0.0803212 |
| UCSize | -0.3807930 | -0.0466403 | -0.0199378 | 0.2042554 | -0.1456529 |
| UCShape | -0.3775825 | -0.0824225 | 0.0335109 | 0.1758656 | -0.1083915 |
| MAF | -0.3327236 | -0.0520944 | -0.4126473 | -0.4931726 | -0.0195690 |
| ECSize | -0.3362340 | 0.1644044 | -0.0877425 | 0.4273836 | -0.6366932 |
| BN | -0.3350675 | -0.2612606 | 0.0006915 | -0.4986177 | -0.1247729 |
| BC | -0.3457474 | -0.2280768 | -0.2130718 | -0.0130473 | 0.2276657 |
| NN | -0.3355914 | 0.0339658 | -0.1342484 | 0.4171135 | 0.6902102 |
| M | -0.2302064 | 0.9055573 | 0.0804922 | -0.2589878 | 0.1050417 |
fviz_eig(all_pca, addlabels = TRUE, ylim = c(0, 75), geom = c("bar", "line"), barfill = "pink",
barcolor="blue",linecolor = "red", ncp = 10) +
labs(title = "PCA for all predictors, excluded NAs in BN predictor",
x = "Principal Components", y = "% of Variance")all_var = get_pca_var(all_pca)
all_var## Principal Component Analysis Results for variables
## ===================================================
## Name Description
## 1 "$coord" "Coordinates for the variables"
## 2 "$cor" "Correlations between variables and dimensions"
## 3 "$cos2" "Cos2 for the variables"
## 4 "$contrib" "contributions of the variables"
cos2 represents the quality of representation for variables on the factor map. It’s calculated as the squared coordinates:
\[cos2 = coord * coord\]
corrplot(all_var$cos2, is.corr=FALSE)contrib contains the contributions (in percentage) of the variables to the principal components. The contribution of a variable (var) to a given principal component is (in percentage):
\[contrib = \frac{cos2 * 100}{total\;cos2\;of\;the\;component}\]
corrplot(all_var$contrib, is.corr=FALSE)Contribution of Variables to Dim1/Dim2:
p1 = fviz_contrib(all_pca, choice = "var", axes=1, fill = "pink", color = "grey", top = 10)
p2 = fviz_contrib(all_pca, choice = "var", axes=2, fill = "skyblue", color = "grey", top = 10)
grid.arrange(p1, p2, ncol = 2)Scores and Loadings in one plot called PCA Biplot:
fviz_pca_biplot(all_pca, col.ind = data2$ClassCat, col = "black",
palette = "jco", geom = "point", repel = TRUE,
legend.title = "Diagnosis", addEllipses = TRUE)Interactive 3D chart - correlation between UCSize, UCShape and BC:
plot_ly(data2, x=~UCSize, y=~UCShape, z=~BC,
color=~ClassCat, mode= "markers",type = "scatter3d") %>%
layout(title = "UCSize, UCShape and BC correlation by Diagnosis")Interactive score plot for PC1 and PC2:
pca_coordinates = as.data.frame(all_pca$x) %>% select(PC1, PC2) %>% cbind(data2[,c(1:9, 12)])
plot_ly(data = pca_coordinates, x = ~PC1, y = ~PC2, type = 'scatter',
mode = 'markers', symbol = ~ClassCat, symbols = c('circle','x','o'),
text = ~paste("CT: ", CT, '<br>UCSize:', UCSize, '<br>UCShape: ', UCShape, '<br>MAF: ', MAF, '<br>ECSize: ', ECSize),
color = ~ClassCat, marker = list(size = 10)) %>%
layout(title = "Score plot for PC1 and PC2")Best Classification Model Selection
Below part demonstrates best classification model selection based on metrics such as Accuracy, Sensitivity and Specificity.
\[Accuracy = \frac{True Positives + True Negatives}{Total Population}\]
\[Sensitivity = \frac{True Positives}{True Positives + False Negatives}\]
\[Specificity = \frac{True Negatives}{True Negatives + False Positives}\]
Load the Data and compute variable importance
Variable importance has been computed based on Random Forest model
List of hyperparameters for selected models is available at this site.
load("models.RData")
plot(varImp(rf_fit), main="Variable importance computed based on Random Forest model")Process the Results
models = list(xgboost_fit, ada_fit, c50_fit, rf_fit, nb_fit, knn_fit, svm_fit, nnet_fit, gbm_fit, logreg_fit)
results_df = data.frame("Accuracy"=rep(0,length(models)),
"Sensitivity"=rep(0,length(models)),
"Specificity"=rep(0,length(models)))
for(i in 1:length(models)) {
pred_mod = predict(models[[i]], newdata = testing)
cm = confusionMatrix(data = pred_mod, reference = testing$Class)
# names(cm)
results_df[i,1] = cm$overall[[1]] # Accuracy
results_df[i,2] = cm$byClass[[1]] # Sensitivity
results_df[i,3] = cm$byClass[[2]] # Specificity
}
rownames(results_df) = c("XGBoost","AdaBoost","c50","RandomForest","NaiveBayes",
"Knn","SVM","NNET","GBM","LogReg")
results_df = results_df %>% arrange(desc(Accuracy))Draw Final Outputs for Model Comparison
dotchart(results_df$Accuracy, labels=row.names(results_df), cex=1.3, main="Accuracy", pch=19)
dotchart(results_df$Sensitivity, labels=row.names(results_df), cex=1.3, main="Sensitivity", pch=19)
dotchart(results_df$Specificity, labels=row.names(results_df), cex=1.3, main="Specificity", pch=19)results_df$Accuracy = cell_spec(results_df$Accuracy, color = ifelse(results_df$Accuracy > 0.97, "green", "black"))
kbl(results_df, escape = F) %>%
kable_paper("striped", full_width = F)| Accuracy | Sensitivity | Specificity | |
|---|---|---|---|
| NNET | 0.982352941176471 | 0.990991 | 0.9661017 |
| AdaBoost | 0.976470588235294 | 0.990991 | 0.9491525 |
| RandomForest | 0.976470588235294 | 0.990991 | 0.9491525 |
| c50 | 0.970588235294118 | 0.981982 | 0.9491525 |
| SVM | 0.970588235294118 | 0.990991 | 0.9322034 |
| NaiveBayes | 0.964705882352941 | 0.981982 | 0.9322034 |
| LogReg | 0.964705882352941 | 0.990991 | 0.9152542 |
| XGBoost | 0.958823529411765 | 0.990991 | 0.8983051 |
| GBM | 0.958823529411765 | 0.990991 | 0.8983051 |
| Knn | 0.764705882352941 | 1.000000 | 0.3220339 |
Conclusion
The main purpose of this presentation was to demonstrate various techniques for basic data analysis.
Significant part consists of classification model selection based on the results of cross validation procedure.